Feature descriptors generated by a sequence of two-pixel intensity comparisons are capable of representing image features tersely and quickly. These binary or Boolean string descriptors, which store a comparison's outcome in a single bit, require a small amount of memory per feature, reducing memory footprint and network transfer bandwidth. Computing and matching these descriptors requires less runtime than alternatives like the Scale-Invariant Feature Transform (SIFT) algorithm and the Speeded Up Robust Features (SURF) algorithm, with comparable matching accuracy.
The Binary Robust Independent Elementary Features (BRIEF) method is notable due to its ability to gain runtime improvements through use of vector instructions. Reduction in memory usage and runtime is suited to the needs of high-FPS real-time vision applications.